Scientific Data and Machine Learning Engineer – 5468 URA

 

As a Scientific Data & Machine Learning Engineer, you will be working on automating data pipeline management, building integrations between different scientific applications and in maintaining a cloud analytics platform as well as translating Machine Learning research into tangible applications to address needs of scientific discovery and product development.

The ideal candidate should have an educational background in Computer science or software engineering and have at least 4+ years of experience in Natural Language Processing (NLP), knowledge graph database and Data Engineering within a scientific organisation. This is a permanent role in Swiss Romandie.

 Main Responsibilities:

 

  • Build & maintain automation of data flows
  • Build & maintain machine learning / advanced analytics applications.
  • Study, research, prototype and validate machine learning applications.
  • Documentation and maintenance of code
  • Partner with Data Scientists / Data Analytics Experts in addressing engineering challenges and ensuring scalability of prototyped applications.
  • Expand and apply your knowledge about advances in data technology and machine learning.

 Qualifications and Experience:

  • Relevant working/residency permit or Swiss/EU-Citizenship required.
  • 4+ years of experience in coding Life Sciences / Pharma / Food / Academia, applying agile development process.
  • Experience building, training, and optimizing neural networks
  • Experience in software development with Python.
  • Experience with Linux and Cloud Services, eg:- AWS or Microsoft Azure.
  • Experience on HTML, CSS, and JavaScript (Angular, React) for UI interactions.
  • Experience working with RESTful and JSON API structure.
  • Strong Relational database skills (SQL, DB connection)
  • Excellent communication skills – in English. Capable of explaining complex technical topics to non-technical audiences
  • Additional technical skills:
  • Experience with data visualization (Plotly, Matplotlib. etc.)
  • Knowledge of NO-SQL and Knowledge Graphs.
  • Machine learning (skimpy, numpy, pandas)
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